The neurodegenerative syndromes comprise a wide spectrum of disease with significant morbidity yet inadequate therapy. A better understanding of the selective neuronal vulnerability, regional structure-function relationships, and systems connectivity could allow us to hone our understanding of pathogenesis, identify avenues of potential therapy, and develop an index of disease. In particular, development and validation of a tool to measure a disease marker could help not only to understand the basic pathogenesis but also with diagnosis (particularly in the early and potentially treatable stages) and the clinical imperatives of staging, prognosis, and evaluation of response to therapeutic interventions. In order to meet feasibility criteria for a future clinical study, we propose to develop, verify, and prepare to validate the "s-image" connectivity-based reference framework as a novel clinical and research tool for DT-MRI analysis. An s-image is an organized rectilinear representation of multidimensional data, such that data for each fiber bundlet (a single-voxel subdivision of a fiber bundle) are represented according to their curvilinear distance from a region of interest (ROI). The s-image framework will allow us: 1) to resolve individual brainstem tracts, 2) to calculate accurate and specific measures of white matter integrity, and 3) to compare values over time, across individuals, and across diagnoses. First we will develop the s-image software framework and perform anatomic validation using in vivo and post-mortem data from the cerebellar peduncles. Then, we will collect preliminary data to design a future study using neurologic and neurocognitive function as probes for structure-function correlations. Correlation of quantitative neurocognitive testing with DT-MRI measures will also allow us to elucidate what contribution 1) frontal involvement, 2) cerebellar involvement, or 3) frontocerebellar disconnection may make to "frontal" executive function, a key factor leading to disability from neurodegenerative disease. We propose that: 1) by verifying that s-images allow us to detect white matter pathology, and 2) by validating the ability of s-images to detect clinically significant pathological cerebellar peduncle changes in complex brainstem regions, it is reasonable to expect that s-images would also be able to detect pathological changes in other complex regions. An important subhypothesis is that the anatomic localization of cognitive domains will allow us to use behavioral measures to predict anatomic patterns of white matter disease in neurodegeneration. A second subhypothesis is that white matter abnormalities may be an early marker of degeneration and thus may precede behavioral or volumetric abnormalities. Although this is an ambitious project, our team is uniquely positioned to succeed because of our multidisciplinary expertise and long history of successful collaboration correlating cerebellar/brainstem imaging and pathophysiology of spinocerebellar ataxias. We believe that this proposal for development and validation of the s-image framework for analysis of brainstem and cerebellar pathology is a highly promising avenue of research that will beget further basic research, establish a basis for improved diagnosis, staging, and management of cerebellar degeneration, and provide an approach applicable to all central nervous system disease. PUBLIC HEALTH RELEVANCE: Project narrative Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and cerebellar ataxia, have a devastating impact on many lives. We propose to develop a disease marker that not only could help to understand the behavior of the brain in disease, but also could be used as a tool for diagnosis, evaluation of the stage of disease, prediction of disease course, and monitoring response to therapy. Specifically, we will develop the "s-image," a new framework for analysis that will take data from diffusion tensor magnetic resonance imaging - a way to observe the directed motion of water along the white matter tracts in the living brain. This has important implications not only for neurodegenerative disease but for all conditions affecting the brain, including strokes, infections, and tumors. [unreadable] [unreadable] [unreadable]